Research output per year
Research output per year
Research output: Contribution to journal › Article › Scientific › peer-review
Advancements in aircraft controller design, paired with increasingly flexible aircraft concepts, create the need for the development of novel (smart) adaptive sensing methods suitable for aeroelastic state estimation. A potentially universal and noninvasive approach is visual tracking. However, many tracking methods require manual selection of initial marker locations at the start of a tracking sequence. This study aims to address the gap by investigating a robust machine learning approach for unsupervised automatic labeling of visual markers. The method uses fast DBSCAN and adaptive image segmentation pipeline with hue-saturation-value color filter to extract and label the marker centers under the presence of marker failure. In a comparative study, the DBSCAN clustering performance is assessed against an alternative clustering method, the disjoint-set data structure. The segmentation-clustering pipeline with DBSCAN is capable of running real-time at 250 FPS on a single camera image sequence with a resolution of 1088×600 pixels. To increase robustness against noise, a novel formulation (the inverse DBSCAN, DBSCAN−1 ) is introduced. This approach is validated on an experimental dataset collected from camera observations of a flexible wing undergoing gust excitations in a wind tunnel, demonstrating an excellent match with the ground truth obtained with a laser vibrometer measurement system.
Original language | English |
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Pages (from-to) | 58-79 |
Number of pages | 22 |
Journal | Journal of Aerospace Information Systems |
Volume | 18 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jan 2021 |
Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review
Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review